Abstract #3754

A GPU-accelerated Extended Phase Graph Algorithm for differentiable optimization and learning

Somnath Rakshit¹, Ke Wang², and Jonathan I Tamir^3,4,5

¹School of Information, The University of Texas at Austin, Austin, TX, United States, ²Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, ³Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States, ⁴Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, United States, ⁵Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States

The Extended Phase Graph Algorithm is a powerful tool for MRI sequence simulation and quantitative fitting, but such simulators are mostly written to run on CPU only and (with some exception) are poorly parallelized. A parallelized simulator compatible with other learning-based frameworks would be a useful tool to optimize scan parameters. Thus, we created an open source, GPU-accelerated EPG simulator in PyTorch. Since the simulator is fully differentiable by means of automatic differentiation, it can be used to take derivatives with respect to sequence parameters, e.g. flip angles, as well as tissue parameters, e.g. T1 and T2.

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